
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    TextToVideoSDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





@skip_mps
class     __UpperCamelCase (							lowerCAmelCase__ ,						unittest.TestCase       ):






     """simple docstring"""







     lowerCAmelCase_							   =			TextToVideoSDPipeline
     lowerCAmelCase_							   =			TEXT_TO_IMAGE_PARAMS
     lowerCAmelCase_							   =			TEXT_TO_IMAGE_BATCH_PARAMS
     # No `output_type`.
     lowerCAmelCase_							   =			frozenset(
         [
             '''num_inference_steps''',
             '''generator''',
             '''latents''',
             '''return_dict''',
             '''callback''',
             '''callback_steps''',
         ]       )



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		UNetaDConditionModel(
           block_out_channels=(32, 64, 64, 64)     ,						layers_per_block=2     ,						sample_size=32     ,						in_channels=4     ,						out_channels=4     ,						down_block_types=('''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''CrossAttnDownBlock3D''', '''DownBlock3D''')     ,						up_block_types=('''UpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''', '''CrossAttnUpBlock3D''')     ,						cross_attention_dim=32     ,						attention_head_dim=4     ,						)
       __SCREAMING_SNAKE_CASE      :	Any					=		DDIMScheduler(
           beta_start=0.0_00_85     ,						beta_end=0.0_12     ,						beta_schedule='''scaled_linear'''     ,						clip_sample=_A     ,						set_alpha_to_one=_A     ,						)
       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	Dict					=		AutoencoderKL(
           block_out_channels=[32, 64]     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']     ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']     ,						latent_channels=4     ,						sample_size=128     ,						)
       torch.manual_seed(0 )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		CLIPTextConfig(
           bos_token_id=0     ,						eos_token_id=2     ,						hidden_size=32     ,						intermediate_size=37     ,						layer_norm_eps=1e-05     ,						num_attention_heads=4     ,						num_hidden_layers=5     ,						pad_token_id=1     ,						vocab_size=1000     ,						hidden_act='''gelu'''     ,						projection_dim=512     ,						)
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		CLIPTextModel(_A )
       __SCREAMING_SNAKE_CASE      :	Dict					=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
       }
       return components



     def  UpperCAmelCase__							(				self							:							Dict     ,						_A							:							List[Any]     ,						_A							:							Tuple=0 ):







       """simple docstring"""




       if str(_A ).startswith('''mps''' ):
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		torch.manual_seed(_A )
       else:
         __SCREAMING_SNAKE_CASE      :	Any					=		torch.Generator(device=_A ).manual_seed(_A )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		{
           '''prompt''': '''A painting of a squirrel eating a burger''',
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''pt''',
       }
       return inputs



     def  UpperCAmelCase__							(				self							:							Tuple ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Dict					=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       __SCREAMING_SNAKE_CASE      :	Dict					=		self.get_dummy_components()
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		TextToVideoSDPipeline(**_A )
       __SCREAMING_SNAKE_CASE      :	int					=		sd_pipe.to(_A )
       sd_pipe.set_progress_bar_config(disable=_A )

       __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.get_dummy_inputs(_A )
       __SCREAMING_SNAKE_CASE      :	Any					=		'''np'''
       __SCREAMING_SNAKE_CASE      :	Dict					=		sd_pipe(**_A ).frames
       __SCREAMING_SNAKE_CASE      :	Tuple					=		frames[0][-3:, -3:, -1]

       assert frames[0].shape == (64, 64, 3)
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		np.array([1_58.0, 1_60.0, 1_53.0, 1_25.0, 1_00.0, 1_21.0, 1_11.0, 93.0, 1_13.0] )

       assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2



     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       self._test_attention_slicing_forward_pass(test_mean_pixel_difference=_A     ,						expected_max_diff=3e-3 )



     @unittest.skipIf(
         torch_device != '''cuda''' or not is_xformers_available()     ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''     ,						)
     def  UpperCAmelCase__							(				self							:							int ):







       """simple docstring"""




       self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=_A     ,						expected_max_diff=1e-2 )



     @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       pass



     @unittest.skip(reason='''Batching needs to be properly figured out first for this pipeline.''' )
     def  UpperCAmelCase__							(				self							:							Any ):







       """simple docstring"""




       pass



     @unittest.skip(reason='''`num_images_per_prompt` argument is not supported for this pipeline.''' )
     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       pass




     def  UpperCAmelCase__							(				self							:							List[str] ):







       """simple docstring"""




       return super().test_progress_bar()






@slow
@skip_mps
class     __UpperCamelCase (							unittest.TestCase       ):






     """simple docstring"""







     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[Any]					=		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy''' )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
       __SCREAMING_SNAKE_CASE      :	str					=		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
       __SCREAMING_SNAKE_CASE      :	Any					=		pipe.to('''cuda''' )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		'''Spiderman is surfing'''
       __SCREAMING_SNAKE_CASE      :	Tuple					=		torch.Generator(device='''cpu''' ).manual_seed(0 )

       __SCREAMING_SNAKE_CASE      :	int					=		pipe(_A     ,						generator=_A     ,						num_inference_steps=25     ,						output_type='''pt''' ).frames
       __SCREAMING_SNAKE_CASE      :	Tuple					=		video_frames.cpu().numpy()

       assert np.abs(expected_video - video ).mean() < 5e-2




     def  UpperCAmelCase__							(				self							:							List[Any] ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	List[str]					=		load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy''' )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		TextToVideoSDPipeline.from_pretrained('''damo-vilab/text-to-video-ms-1.7b''' )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		pipe.to('''cuda''' )

       __SCREAMING_SNAKE_CASE      :	List[str]					=		'''Spiderman is surfing'''
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		torch.Generator(device='''cpu''' ).manual_seed(0 )

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		pipe(_A     ,						generator=_A     ,						num_inference_steps=2     ,						output_type='''pt''' ).frames
       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		video_frames.cpu().numpy()

       assert np.abs(expected_video - video ).mean() < 5e-2




"""simple docstring"""



import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

lowercase_							=							(
    '4S 3H 2C 7S 5H',
    '9D 8H 2C 6S 7H',
    '2D 6D 9D TH 7D',
    'TC 8C 2S JH 6C',
    'JH 8S TH AH QH',
    'TS KS 5S 9S AC',
    'KD 6S 9D TH AD',
    'KS 8D 4D 9S 4S',  # pair
    '8C 4S KH JS 4D',  # pair
    'QH 8H KD JH 8S',  # pair
    'KC 4H KS 2H 8D',  # pair
    'KD 4S KC 3H 8S',  # pair
    'AH 8S AS KC JH',  # pair
    '3H 4C 4H 3S 2H',  # 2 pairs
    '5S 5D 2C KH KH',  # 2 pairs
    '3C KH 5D 5S KH',  # 2 pairs
    'AS 3C KH AD KH',  # 2 pairs
    '7C 7S 3S 7H 5S',  # 3 of a kind
    '7C 7S KH 2H 7H',  # 3 of a kind
    'AC KH QH AH AS',  # 3 of a kind
    '2H 4D 3C AS 5S',  # straight (low ace)
    '3C 5C 4C 2C 6H',  # straight
    '6S 8S 7S 5H 9H',  # straight
    'JS QS 9H TS KH',  # straight
    'QC KH TS JS AH',  # straight (high ace)
    '8C 9C 5C 3C TC',  # flush
    '3S 8S 9S 5S KS',  # flush
    '4C 5C 9C 8C KC',  # flush
    'JH 8H AH KH QH',  # flush
    '3D 2H 3H 2C 2D',  # full house
    '2H 2C 3S 3H 3D',  # full house
    'KH KC 3S 3H 3D',  # full house
    'JC 6H JS JD JH',  # 4 of a kind
    'JC 7H JS JD JH',  # 4 of a kind
    'JC KH JS JD JH',  # 4 of a kind
    '2S AS 4S 5S 3S',  # straight flush (low ace)
    '2D 6D 3D 4D 5D',  # straight flush
    '5C 6C 3C 7C 4C',  # straight flush
    'JH 9H TH KH QH',  # straight flush
    'JH AH TH KH QH',  # royal flush (high ace straight flush)
)

lowercase_							=							(
    ('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
    ('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
    ('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
    ('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
    ('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
    ('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
    ('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
    ('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
    ('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
    ('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
    ('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
    ('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
    ('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
    ('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
    ('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
    ('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
    ('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
    ('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
    ('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
    ('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
    ('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
    ('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
    ('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
    ('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
    ('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
    ('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
    ('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
    ('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
    ('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
    ('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
    ('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
    ('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
    ('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)

lowercase_							=							(
    ('2H 3H 4H 5H 6H', True),
    ('AS AH 2H AD AC', False),
    ('2H 3H 5H 6H 7H', True),
    ('KS AS TS QS JS', True),
    ('8H 9H QS JS TH', False),
    ('AS 3S 4S 8S 2S', True),
)

lowercase_							=							(
    ('2H 3H 4H 5H 6H', True),
    ('AS AH 2H AD AC', False),
    ('2H 3H 5H 6H 7H', False),
    ('KS AS TS QS JS', True),
    ('8H 9H QS JS TH', True),
)

lowercase_							=							(
    ('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 14]),
    ('2H 5D 3C AS 5S', False, [14, 5, 5, 3, 2]),
    ('JH QD KC AS TS', False, [14, 13, 12, 11, 10]),
    ('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)

lowercase_							=							(
    ('JH AH TH KH QH', 0),
    ('JH 9H TH KH QH', 0),
    ('JC KH JS JD JH', 7),
    ('KH KC 3S 3H 3D', 6),
    ('8C 9C 5C 3C TC', 0),
    ('JS QS 9H TS KH', 0),
    ('7C 7S KH 2H 7H', 3),
    ('3C KH 5D 5S KH', 2),
    ('QH 8H KD JH 8S', 1),
    ('2D 6D 9D TH 7D', 0),
)

lowercase_							=							(
    ('JH AH TH KH QH', 23),
    ('JH 9H TH KH QH', 22),
    ('JC KH JS JD JH', 21),
    ('KH KC 3S 3H 3D', 20),
    ('8C 9C 5C 3C TC', 19),
    ('JS QS 9H TS KH', 18),
    ('7C 7S KH 2H 7H', 17),
    ('3C KH 5D 5S KH', 16),
    ('QH 8H KD JH 8S', 15),
    ('2D 6D 9D TH 7D', 14),
)
def      UpperCAmelCase     (       )						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_							,	lowerCAmelCase_      							=			randrange(len(_lowercase	)	), randrange(len(_lowercase	)	)
	lowerCAmelCase_      							=			['''Loss''', '''Tie''', '''Win'''][(play >= oppo) + (play > oppo)]
	lowerCAmelCase_							,	lowerCAmelCase_      							=			SORTED_HANDS[play], SORTED_HANDS[oppo]
	return hand, other, expected
def      UpperCAmelCase     (       _lowercase :   int = 1_0_0	)						-> int:


	"""simple docstring"""




	return (generate_random_hand() for _ in range(_lowercase	))
@pytest.mark.parametrize('''hand, expected'''			,   _lowercase	)
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]			,   _lowercase :   Optional[int]	)						-> str:


	"""simple docstring"""




	assert PokerHand(_lowercase	)._is_flush() == expected
@pytest.mark.parametrize('''hand, expected'''			,   _lowercase	)
def      UpperCAmelCase     (       _lowercase :   List[Any]			,   _lowercase :   List[str]	)						-> Optional[Any]:


	"""simple docstring"""




	assert PokerHand(_lowercase	)._is_straight() == expected
@pytest.mark.parametrize('''hand, expected, card_values'''			,   _lowercase	)
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   Any			,   _lowercase :   Union[str, Any]	)						-> str:


	"""simple docstring"""




	lowerCAmelCase_      							=			PokerHand(_lowercase	)
	assert player._is_five_high_straight() == expected
	assert player._card_values == card_values
@pytest.mark.parametrize('''hand, expected'''			,   _lowercase	)
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   Union[str, Any]	)						-> Optional[Any]:


	"""simple docstring"""




	assert PokerHand(_lowercase	)._is_same_kind() == expected
@pytest.mark.parametrize('''hand, expected'''			,   _lowercase	)
def      UpperCAmelCase     (       _lowercase :   Tuple			,   _lowercase :   int	)						-> str:


	"""simple docstring"""




	assert PokerHand(_lowercase	)._hand_type == expected
@pytest.mark.parametrize('''hand, other, expected'''			,   _lowercase	)
def      UpperCAmelCase     (       _lowercase :   int			,   _lowercase :   str			,   _lowercase :   Tuple	)						-> Union[str, Any]:


	"""simple docstring"""




	assert PokerHand(_lowercase	).compare_with(PokerHand(_lowercase	)	) == expected
@pytest.mark.parametrize('''hand, other, expected'''			,   generate_random_hands()	)
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   Dict			,   _lowercase :   Dict	)						-> Union[str, Any]:


	"""simple docstring"""




	assert PokerHand(_lowercase	).compare_with(PokerHand(_lowercase	)	) == expected
def      UpperCAmelCase     (       )						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			[PokerHand(_lowercase	) for hand in SORTED_HANDS]
	lowerCAmelCase_      							=			poker_hands.copy()
	shuffle(_lowercase	)
	lowerCAmelCase_      							=			chain(sorted(_lowercase	)	)
	for index, hand in enumerate(_lowercase	):
		assert hand == poker_hands[index]
def      UpperCAmelCase     (       )						-> Any:


	"""simple docstring"""




	lowerCAmelCase_      							=			[PokerHand('''2D AC 3H 4H 5S'''	), PokerHand('''2S 3H 4H 5S 6C'''	)]
	pokerhands.sort(reverse=_lowercase	)
	assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"
def      UpperCAmelCase     (       )						-> List[str]:


	"""simple docstring"""




	lowerCAmelCase_      							=			PokerHand('''2C 4S AS 3D 5C'''	)
	lowerCAmelCase_      							=			True
	lowerCAmelCase_      							=			[5, 4, 3, 2, 1_4]
	for _ in range(1_0	):
		assert pokerhand._is_five_high_straight() == expected
		assert pokerhand._card_values == expected_card_values





def      UpperCAmelCase     (       )						-> Dict:


	"""simple docstring"""




	lowerCAmelCase_      							=			0
	lowerCAmelCase_      							=			os.path.abspath(os.path.dirname(_lowercase	)	)
	lowerCAmelCase_      							=			os.path.join(_lowercase			,   '''poker_hands.txt'''	)
	with open(_lowercase	) as file_hand:
		for line in file_hand:
			lowerCAmelCase_      							=			line[:1_4].strip()
			lowerCAmelCase_      							=			line[1_5:].strip()
			lowerCAmelCase_							,	lowerCAmelCase_      							=			PokerHand(_lowercase	), PokerHand(_lowercase	)
			lowerCAmelCase_      							=			player.compare_with(_lowercase	)
			if output == "Win":
				answer += 1
	assert answer == 3_7_6